d2cml-ai/CausalAI-Course
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Causal InferenceData AnalysisData PreprocessingData VisualizationFeature EngineeringJupyter Notebook
LeidyMezarina
PROFILE
Leidymezarina
Developed a reproducible statistical analysis notebook for the d2cml-ai/CausalAI-Course repository, focusing on end-to-end data analysis pipelines. The work included environment setup, preprocessing of categorical variables, and feature engineering with interaction terms, followed by modeling using Lasso regression and decision trees. Implemented and documented workflows in Python and Jupyter Notebook, leveraging scikit-learn for machine learning tasks. Maintained the notebook by updating cell IDs to improve traceability and maintainability. This contribution enhanced the reproducibility and scalability of course analytics, supporting rapid experimentation, clearer student guidance, and streamlined curriculum updates, while demonstrating strong skills in data preprocessing and statistical modeling.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
604
Activity Months1
Your Network
91 peopleWork History
November 2024
3 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered a reproducible Statistical Analysis notebook introducing end-to-end data analysis pipelines (environment setup, preprocessing of categorical variables, feature engineering with interaction terms) and modeling pipelines with Lasso regression and decision trees. Performed notebook housekeeping (updating cell IDs) to improve maintainability. No major bugs fixed this month; primary focus was on delivering a robust, reusable analytics workflow. Business impact: enhanced reproducibility and scalability of course analytics, enabling rapid experimentation, clearer student guidance, and streamlined curriculum updates. Technologies/skills demonstrated: Python, Jupyter notebooks, scikit-learn (Lasso, DecisionTree), data preprocessing for categorical variables, feature engineering, and version control.
3 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered a reproducible Statistical Analysis notebook introducing end-to-end data analysis pipelines (environment setup, preprocessing of categorical variables, feature engineering with interaction terms) and modeling pipelines with Lasso regression and decision trees. Performed notebook housekeeping (updating cell IDs) to improve maintainability. No major bugs fixed this month; primary focus was on delivering a robust, reusable analytics workflow. Business impact: enhanced reproducibility and scalability of course analytics, enabling rapid experimentation, clearer student guidance, and streamlined curriculum updates. Technologies/skills demonstrated: Python, Jupyter notebooks, scikit-learn (Lasso, DecisionTree), data preprocessing for categorical variables, feature engineering, and version control.
November 2024
Activity
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Quality Metrics
Correctness73.4%
Maintainability73.4%
Architecture73.4%
Performance66.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Causal InferenceData AnalysisData PreprocessingData VisualizationFeature EngineeringJupyter NotebookMachine LearningPandasPythonRegression AnalysisScikit-learnStatistical Modeling
Repositories Contributed To
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